code: def test_create_tf_model(
      name_from_base, sagemaker_session, coach_tensorflow_version
  ):
      container_log_level = '"logging.INFO"'
      source_dir = "s3://mybucket/source"
      rl = RLEstimator(
          entry_point=SCRIPT_PATH,
          role=ROLE,
          sagemaker_session=sagemaker_session,
          instance_count=INSTANCE_COUNT,
          instance_type=INSTANCE_TYPE,
          toolkit=RLToolkit.COACH,
          toolkit_version=coach_tensorflow_version,
          framework=RLFramework.TENSORFLOW,
          container_log_level=container_log_level,
          source_dir=source_dir,
      )
  
      rl.fit(inputs="s3://mybucket/train", job_name="new_name")
  
      model_name = "model_name"
      name_from_base.return_value = model_name
      model = rl.create_model()
  
      supported_versions = TOOLKIT_FRAMEWORK_VERSION_MAP[
          RLToolkit.COACH.value
      ]
      framework_version = supported_versions[coach_tensorflow_version][
          RLFramework.TENSORFLOW.value
      ]
  
      assert isinstance(model, TensorFlowModel)
      assert model.sagemaker_session == sagemaker_session
      assert model.framework_version == framework_version
      assert model.role == ROLE
      assert model.name == model_name
      assert model._container_log_level == container_log_level
      assert model.vpc_config is None
  
      call_args = name_from_base.call_args_list[0][0]
      assert call_args[0] in (
          "sagemaker-rl-tensorflow",
          "sagemaker-rl-coach-container",
      )
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import pytest
  from mock import MagicMock, Mock, patch
  from sagemaker.mxnet import MXNetModel, MXNetPredictor
  from sagemaker.rl import RLEstimator, RLFramework, RLToolkit, TOOLKIT_FRAMEWORK_VERSION_MAP
  from sagemaker.session_settings import SessionSettings
  from sagemaker.tensorflow import TensorFlowModel, TensorFlowPredictor
label_desc: Use image node_id 5 for calling a global function with example usage: test_create_tf_model(name_from_base, sagemaker_session, coach_tensorflow_version) without return types